Ainiwaer et al. BMC Medical Genetics(2020) 21:212
https://doi.org/10.1186/s12881-020-01144-5

RESEARCH ARTICLE

Open Access

Genetic polymorphisms of PGF and TNFAIP2
genes related to cervical cancer risk among
Uygur females from China
Zumurelaiti Ainiwaer†, Reyilanmu Maisaidi†, Jing Liu, Lili Han* , Sulaiya Husaiyin, Jing Lu and Mayinuer Niyazi

Abstract
Background: PGF and TNFAIP2 are important angiogenic factors, which were abnormal expression in cervicalcancer (CC). However, there is currently no report investigating the relationship of PGF and TNFAIP2 gene
polymorphisms to CC risk.
Methods: We conducted a case-control study of 342 CC patients and 498 cancer-free controls in a Chinese Uygur
female population. Three SNPs (PGF rs8019391, PGF rs2268615, and TNFAIP2 rs710100) were selected and genotyped
to assess the possible association of PGF and TNFAIP2 polymorphisms with CC susceptibility. Logistic regression
analysis adjusted by age was used.
Results: PGF rs2268615 (OR = 1.39, 95% CI = 1.04–1.86, p = 0.024) and TNFAIP2 rs710100 (OR = 1.44, 95% CI =1.07–
1.95, p = 0.018) polymorphisms were associated with the increased risk of CC. Moreover, T allele of PGF rs8019391
was highly represented in patients with stage III–IV compared with stage I-II (OR = 2.17, p = 4.58 × 10− 4). MDR
analysis revealed a positive interaction between the SNPs.
Conclusion: Our data indicated that PGF rs2268615, and TNFAIP2 rs710100 polymorphisms might be risk factors for
CC susceptibility, which contributed to the increased risk of CC.
Trial registration: Not applicable.
Keywords: PGF, TNFAIP2, Polymorphism, Cervical cancer

Background
Worldwide, cervical cancer (CC) is the fourth most common cancer, with an estimated 570,000 cases and 311,
000 deaths in 2018 [1]. Cervical cancer is the most common cancer of female genital system in China and theincidence of CC tends to be younger [2]. A wide range
of inter-individual genetic variability and other pathogeneses might contribute to cervical carcinogenesis. Accumulating evidence suggested that single nucleotide* Correspondence: hanliliabcd@163.com†
Zumurelaiti Ainiwaer and Reyilanmu Maisaidi are co-first authors.
Department of Gynecology, Xinjiang Medical University, People’s Hospital of
Xinjiang Uygur Autonomous Region, No 91 Tianqi Road, Urumqi, Xinjiang
830001, China

polymorphisms (SNPs) in tumor-associated genes played
important role in the genetic susceptibility to CC, suchas FAS, IL-17A, IL-17F and HOTAIR polymorphisms[3–5].
PGF (placental growth factor) gene, also named PLGF,
encodes a homologous of vascular endothelial growth
factor. PGF has been reported as a potent stimulator incancer invasion by activating angiogenesis [6]. In
addition, the overexpression of PGF is correlated withtumor stage, cancer progression and metastasis [7].
TNFAIP2 (TNF alpha induced protein 2) is a primary response gene of TNFα, and the expression of TNFAIP2 isregulated by various transcription factors and signaling
pathways, including NF-κB, KLF5 and retinoic acid

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Ainiwaer et al. BMC Medical Genetics(2020) 21:212

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pathways [8]. TNFAIP2 is an important angiogenic factor, and is significantly associated with intratumoral
microvessel density [9]. In addition, previous studies reported the abnormal expression of PGF and TNFAIP2 inhuman cancer, including CC [6, 10]. However, there is
no report about the associations between PGF andTNFAIP2 polymorphisms and CC risk. We hypothesizedthat genetic variants in PGF and TNFAIP2 could contribute to CC susceptibility.
Here, PGF rs8019391 and rs2268615, and TNFAIP2
rs710100 were selected as candidate SNPs from the
NCBI dbSNP database (http://www.ncbi.nlm.nih.gov/
projects/SNP) and the 1000 Genomes Project data
(http://www.internationalgenome.org/), based on minor
allele frequency (MAF) of at least 5% in Chinese populations, with a pairwise r2 > 0.80 and the call rate of genotyping > 95%. The aim of our study was to investigate
the possible association between three SNPs (PGF
rs8019391, PGF rs2268615, and TNFAIP2 rs710100) andthe risk of CC in Chinese Uygur female population.

Subjects and methods
Study participantsThis study protocol was approved by the Ethics Committee of the People’s Hospital of Xinjiang Uygur
Autonomous Region and was in accordance with theguidelines of the Declaration of Helsinki. Written informed consents were obtained from all participants.
Three hundred fourty-two cervical cancer patients(cases, 43.27 ± 11.78 years) and 498 age-matched cancerfree individuals (controls, 43.46 ± 13.03 years) were enrolled from the People’s Hospital of Xinjiang Uygur Autonomous Region, as shown in Table 1. All recruitedsubjects were unrelated ethnic Han Chinese females. Patients were newly diagnosed and histopathologically
Table 1 Characteristics of patients with cervical cancer andcontrols
Cases(n = 342)

Controls(n = 498)

p

Mean ± SD (years)

43.27 ± 11.78

43.46 ± 13.03

0.832

> 43

176 (51.5%)263 (52.8%)≤ 43

166 (48.5%)235 (47.2%)

Characteristics
Age

HPV status
Negative

51 (14.9%)

Positive

195 (57.0%)

Missing

96 (28.1%)

Stage
I-II

132 (38.6%)

III-IV80 (23.4%)

Missing

130 (38.0%)

confirmed primary cervical cancer according to the clinical staging standards of the International Federation of
Gynecology and Obstetrics (FIGO). Among patients, 132cases were stage I–II, 80 cases were stage III–IV and 130
cases were missing. Patients with any history of othercancers, radiotherapy, chemotherapy, or surgery and inflammatory diseases were excluded. The controls wererecruited from the health checkup in the same hospitalduring the same period. Controls had no any history ofcancers and diseases of the liver, kidneys, heart, brain,
and vascular system, as well as gynecological inflammatory diseases. Demographic characteristics and clinicalinformation were collected from the standardized
questionnaires.
SNP selection and genotyping

Peripheral venous blood samples (5 mL) were obtained
from all subjects and stored in EDTA-coated tubes.
Genomic DNA was extracted using the GoldMag DNA
Purification Kit (GoldMag Co. Ltd., Xi′an City, China)
according to the manufacturer’s protocol, then quantified by NanoDrop 2000 (Thermo Scientifc, Waltham,
MA, USA), and stored at − 20°Cfor further experiments.
PGF rs8019391 and rs2268615, and TNFAIP2 rs710100
were selected as candidate from the NCBI dbSNP database (http://www.ncbi.nlm.nih.gov/projects/SNP) andthe 1000 Genomes Project data (http://www.internationalgenome.org/), based on minor allele frequency (MAF)of at least 5% in Chinese populations, with a pairwise
r2 > 0.80, and the call rate of genotyping > 95%. SNPs.
miRNASNP_v2 database (http://bioinfo.life.hust.edu.cn/
miRNASNP2/index.php) and HaploReg v4.1 (https://pubs.broadinstitute.org/mammals/haploreg/
haploreg.
php) were used to predict the potential function of thesepolymorphisms (Supplementary Table 1). These candidate SNPs were genotyped with Agena MassARRAY system (Agena, San Diego, CA, U.S.A.) as described
previously [11, 12], and performed by two laboratory
technicians in a double-blinded fashion. The primers for
PCR amplification and single base extension were designed using the Assay Design 3.0 software (Supplementary Table 2). For quality control, approximately 10% of
the samples were randomly selected and repeated genotyping, and 100% concordance rate was observed.
Data analysis

All statistical analysis was performed using SPSS version
18.0 (SPSS Inc., Chicago, IL, USA) and PLINK software.
A student’s t-test was performed to analyze the differences in the age distribution between patients and controls. The Hardy–Weinberg equilibrium (HWE) wastested by a goodness-of-fit χ2 test for each SNP among
the control subjects. The genotype and allele frequencies
between two groups were compared usingχ2 test. The

Ainiwaer et al. BMC Medical Genetics(2020) 21:212

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association of candidate SNPs with CC risk was assessedby odds ratios (ORs) and 95% confidence intervals (CIs)using logistic regression analysis for both combined andrespective genotype [13]. Stratification analysis by demographic and clinic variables was also performed to assess
the genetic association. Multifactor dimensionality reduction (MDR) (version 3.0.2) was used to evaluate the
SNP–SNP interactions in CC risk [14]. A two-tailed pvalue < 0.05 was considered to be statistically significant
for all the analyses.

Results
The results of genotyping was shown in Additional file 2.
In Supplementary Table 3, the MAF of PGF rs8019391
and rs2268615, and TNFAIP2 rs710100 between the caseand control groups were listed. The genotype distribution of these SNPs in controls were in accordance with
the Hardy-Weinberg equilibrium (p > 0.05). The call rate
for rs8019391, rs2268615 and rs710100 were 100, 99.7

and 99.1%, respectively. The MAFs distribution of PGF
rs2268615-A allele and TNFAIP2 rs710100-A allele werehigher in the case group, which increased the risk of CC(rs2268615, A vs C, OR = 1.27, 95% CI = 1.03–1.58, p =0.029; and rs710100, A vs G, OR = 1.23, 95% CI = 1.01–
1.50, p = 0.043).
The results of multiple genetic model adjusted by age
revealed PGF rs2268615 and TNFAIP2 rs710100 conferred to the increased CC risk (Table 2). PGF rs2268615
was associated with an increased risk of CC underheterozygote (OR = 1.39, 95% CI = 1.04–1.86, p = 0.024),
dominant (OR = 1.40, 95% CI = 1.06–1.84, p = 0.018) and
log-additive (OR = 1.29, 95% CI =1.03–1.61, p = 0.027)
models. For rs710100 in TNFAIP2, compared with GG
genotype, GA genotype (OR = 1.44, 95% CI =1.07–1.95,
p = 0.018) and GA + AA genotype (OR = 1.42, 95% CI =1.07–1.89, p = 0.016) increased 1.44-fold and 1.42-fold CC
risk, respectively. Moreover, the result of the additivemodel also showed an increased risk of CC (rs710100,

Table 2 Relationships between the candidate SNPs and cervical cancer risk
Gene
SNP ID

Model

Genotype

Case

Control

PGF
rs8019391

Genotype

CC

208

327

1.00

CT

119

145

1.29 (0.96–1.74)

0.093

TT

15

26

0.91 (0.47–1.76)

0.777

CC

208

327

1.00

0.150

CT-TT

134

171

1.23 (0.93–1.64)

CC-CT

327

472

1.00

TT

15

26

0.83 (0.43–1.6)

OR (95%CI)

Dominant

Recessive

PGF
rs2268615

p

0.585

Log-additive

–

–

–

1.13 (0.89–1.42)

Genotype

CC

160

273

1.00

CA

156

191

1.39 (1.04–1.86)

0.024

AA

26

31

1.43 (0.82–2.49)

0.209
0.018

Dominant

0.324

CC

160

273

1.00

CA-AA

182

222

1.40 (1.06–1.84)

CC-CA

316

464

1.00

AA

26

31

1.23 (0.72–2.11)

Log-additive

–

–

–

1.29 (1.03–1.61)

Genotype

GG

118

210

1.00

GA

171

211

1.44 (1.07–1.95)

0.018

AA

53

69

1.37 (0.89–2.08)

0.150
0.016

Recessive

TNFAIP2
rs710100

Adjusted by age and gender

Dominant

Recessive

Log-additive

GG

118

210

1.00

GA-AA

224

280

1.42 (1.07–1.89)

GG-GA

289

421

1.00

AA

53

69

1.12 (0.76–1.65)

–

–

–

1.23 (1.00–1.50)

SNP single nucleotide polymorphism, OR odds ratio; 95% CI 95% confidence interval
p values were calculated by logistic regression analysis with adjustments for age and genderp < 0.05 means the data is statistically significant

0.453

0.027

0.576

0.046

Ainiwaer et al. BMC Medical Genetics(2020) 21:212

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OR = 1.23, 95% CI = 1.00–1.50, p = 0.046). However, there
was no significant association between PGF rs8019391
and CC susceptibility.
Age stratification displayed that PGF rs2268615 and
TNFAIP2 rs710100 increased the risk of CC among
women at age ≤ 43 years (Table 3). After calculating
the ORs for the allele (OR = 1.38, p = 0.041 and OR =1.42, p = 0.018, respectively), genotype (CA vs CC,
OR = 1.55, p = 0.039; and AA vs GG, OR = 1.97, p =0.031, respectively), dominant (OR = 1.56, p = 0.030;
and OR = 1.57, 95%, p = 0.034, respectively), and logadditive (OR = 1.40, p = 0.042; and OR = 1.42, p =0.020) genetic models, they all displayed the genetic
association of PGF rs2268615 and TNFAIP2 rs710100
with CC susceptibility.
Subsequently, stratification analysis by tumor stage
showed that the risk effect for PGF rs8019391 appeared
to be more prominent in the subset of patients withstage III + IV (Table 4). Compared with the C allele,rs8019391 T allele was highly represented in patientswith stage III–IV as compared to patients with stage I–II
under the allele (OR = 2.17, p = 4.58 × 10− 4), heterozygote (OR = 2.34, p = 0.005), homozygote (OR = 5.76, p =0.015), dominant (OR = 2.59, p = 0.001), recessive (OR =4.13, p = 0.045), and log-additive models (OR = 2.36,
p < 0.001).
Subsequently, MDR analysis was implemented to assess
the impact of SNP-SNP interaction. Association of higher
order interaction with CC risk was summarized in Fig. 1.
The result revealed the additive effect between TNFAIP2
rs710100-GA, PGF rs2268615-CA, and PGF rs8019391CT on conferring risk towards the susceptibility to CC.
The result of dendrogram and the Fruchterman-Reingold(Fig. 2) showed that PGF rs2268615, TNFAIP2 rs710100,
PGF rs8019391 exhibited a strong synergy effect on CC
risk. Table 5 showed that TNFAIP2 rs710100 was the best
single-locus model to predict the risk of CC (testing accuracy = 0.508, CVC = 6/10, p = 0.014). The best two-locus
model was the combination of PGF rs2268615 and TNFA
IP2 rs710100 (testing accuracy = 0.536, CVC = 9/10, p <0.0001. The three-locus model included TNFAIP2
rs710100, PGF rs2268615, and PGF rs8019391 (testing accuracy = 0.550, CVC = 10/10, p < 0.0001).

Discussion
In this case–control study, we assessed the association
between three SNPs (PGF rs8019391, PGF rs2268615,
and TNFAIP2 rs710100) and CC susceptibility in the

Table 3 Relationships between the candidate SNPs and cervical cancer risk according to the stratification by age
SNP ID
PGF
rs2268615

Model
AlleleGenotype

Dominant

Recessive

TNFAIP2
rs710100

Genotype≤ 43 years> 43 years
Case

Control

OR (95%CI)

p

Case

Control

OR (95%CI)

p

C

253

393

1.00

0.303

223

344

1.00

0.041

A

99

131

1.17 (0.87–1.59)

109

122

1.38 (1.01–1.88)

CC

89

147

1.00

71

126

1.00

CA

75

99

1.25 (0.84–1.86)

0.282

81

92

1.55 (1.02–2.36)

0.039

AA

12

16

1.22 (0.55–2.70)

0.626

14

15

1.62 (0.74–3.56)

0.228

0.269

0.030

CC

89

147

1.00

CA-AA

87

115

1.24 (0.85–1.82)

CC-CA

164

246

1.00

AA

12

16

1.11 (0.51–2.41)

Log-additive

–

–

–

1.17 (0.86–1.60)

Allele

G

217

331

1.00

A

135

191

GG

64

111

Genotype

Dominant

Recessive

Log-additive

0.795

71

126

1.00

95

107

1.56 (1.05–2.33)

152

218

1.00

14

15

1.31 (0.61–2.81)

0.481

0.317

–

–

1.40 (1.01–1.92)

0.042

0.597

190

300

1.00

0.018

1.08 (0.82–1.43)

142

158

1.42 (1.06–1.90)

1.00

54

99

1.00

GA

89

109

1.41 (0.93–2.14)

0.105

82

102

1.46 (0.94–2.27)

0.092

AA

23

41

0.96 (0.53–1.74)

0.890

30

28

1.97 (1.07–3.63)

0.031

0.208

0.034

GG

64

111

1.00

GA-AA

112

150

1.29 (0.87–1.91)

GG-GA

153

220

1.00

AA

23

41

0.80 (0.46–1.38)

–

–

–

1.07 (0.81–1.41)

SNP single nucleotide polymorphism, OR odds ratio, 95% CI 95% confidence interval
p values were calculated by logistic regression analysis with adjustments for age
p < 0.05 indicates statistical significance

0.418

0.635

54

99

1.00

112

130

1.57 (1.03–2.38)

136

201

1.00

30

28

1.59 (0.91–2.79)––

1.42 (1.06–1.90)

0.103

0.020

Ainiwaer et al. BMC Medical Genetics(2020) 21:212

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Table 4 Relationship of clinical stage with PGF rs8019391 polymorphism in cervical cancer patients adjusted by age
SNP ID

Model

Genotype

I-II

III-IVOR (95%CI)prs8019391

Allele

C

110

220

1.00

4.58 × 10−4

T

50

44

2.27 (1.43–3.62)

Codominant

Dominant

Recessive

Log-additive

CC

37

91

1.00

CT

36

38

2.34 (1.29–4.25)

0.005

TT

7

3

5.76 (1.41–23.52)

0.015

CC

37

91

1.00

0.001

CT-TT

43

41

2.59 (1.46–4.60)

CC-CT

73

129

1.00

TT

7

3

4.13 (1.04–16.45)

–

–

–

2.36 (1.45–3.86)

0.045< 0.001

SNP single nucleotide polymorphism, OR odds ratio, 95% CI 95% confidence interval
p values were calculated by logistic regression analysis with adjustments for age
p < 0.05 indicates statistical significance

Chinese Uygur female population. We found that PGF
rs2268615, and TNFAIP2 rs710100 were associated with
the increased risk of CC. Our findings also suggested
some possible interaction between genetic variations(PGF rs2268615 and TNFAIP2 rs710100) and age in CC
risk. Moreover, the risk effect for PGF rs8019391

appeared to be more prominent in patients with stage
III + IV. To the best of our knowledge, this is the first report describing that PGF and TNFAIP2 polymorphisms
might be risk factor for CC.
PGF, located at 14q24.3, belongs to the vascular endothelial growth factor family and presents on various cellFig. 1 Summary of MDR gene-gene interaction. Each cell shows counts of “case” on left and “control” on rightAiniwaer et al. BMC Medical Genetics(2020) 21:212

Page 6 of 7

Fig. 2 SNP-SNP interaction dendrogram and Fruchterman-Reingold

types. The expression of PGF in tissue or plasma of cancer patients was upregulated in most human tumor
types, including gallbladder, gastric and prostate cancers.
PGF regulates certain cellular processes such as survival,
vascular endothelial cell growth, invasiveness, and PGF
is also involved in pathological angiogenesis and metastasis [15–17]. Previous researches indicated that PGF
was overexpressed in CC tissues, serum and vaginal lavage compared with adjacent normal tissues or normal
women group [6, 18]. PGF promotes migration by regulating the expression of epithelial-mesenchymaltransition-related protein in CC [6]. These evidence led
us to propose the hypothesis that PGF could be ofimportance in CC occurrence. In our study, we firstly
assess the association of PGF polymorphisms(rs8019391 and rs2268615) with CC susceptibility,and found that rs2268615 conferred the increased riskof CC, and rs8019391 was a risk factor for patientswith stage III-IV. A retrospective population-based
study showed that 5-year relative survival rates of CC
were 90.9, 71.0, 41.7, and 7.8% for the stage I, II, III,and IV, respectively [19]. Therefore, it is highly speculated that PGF rs8019391 polymorphism may affectCC progression. Further large-scale studies are needed
to verify our findings.
TNFAIP2 gene (also named B94) is located on
chromosome 14q32, and encodes TNFα-inducible protein
2. TNFAIP2 participates in the NFκB and KLF5 signaling
pathway to regulate cell inflammatory, angiogenesis, cellproliferation, migration and invasion [20, 21]. The expression of TNFAIP2 was found to be abnormal in various
cancers, including breast cancer, esophageal squamouscell carcinoma, and glioma [20, 22, 23]. The expression of
TNFAIP2 was significantly increased in CC tissues

compared with normal tissues based on TCGA (The Cancer Genome Atlas) database [8]. In addition, the disruption of TNFAIP2 cytokine/retinoic acid-inducible genethrough viral integration contributed to the rapid progression of CC [10]. These evidence suggested that TNFAIP2
might play an important role in the progression of CC. In
this study, we found that TNFAIP2 rs710100 was associated with an increased risk of CC. Rs710100 in the 3′UTRof TNFAIP2, located at the predicted miRNAs-bindingsites, was related to an increased risk of CC. Specifically,
rs1064607 putatively affects the binding of miR-155 [24],whose abnormal expression in CC was correlated with
FIGO stage, lymph nodes metastasis, and vascular invasion [25]. Therefore, we proposed that rs710100 in 3′UTRof TNFAIP2 might affect TNFAIP2 expression in CC by
disturbing mRNA stability or miRNA binding activity,
thus causing a higher risk of CC. However, it should be
confirmed in further functional studies.
In spite of interesting findings on the associationof PGF and TNFAIP2 polymorphisms with CC risk,
several limitations need to be addressed. First, there
may be selection and information bias since theretrospective study was designed as a hospital-based
case–control study. Second, due to insufficient dataof HPV screening results and lifestyle data (e.g.,smoking), we could not evaluate HPV infection andlifestyle as the potential confounder in the risk estimation of CC.

Conclusion
In conclusion, our findings indicated a relationship between PGF rs2268615, and TNFAIP2 rs710100 and theincreased CC susceptibility in the Chinese Uygur females. Considering that this is the first report on the

Table 5 SNP–SNP interaction models of the PGF and TNFAIP2 genes analyzed by the MDR method
Model

Training Bal. Acc.

Testing Bal. Acc.

CVC

OR (95% CI)

p

TNFAIP2 rs710100

0.544

0.508

6/10

1.46 (1.08–1.97)

0.014

PGF rs2268615, TNFAIP2 rs710100

0.564

0.536

9/10

1.91 (1.41–2.59)< 0.0001

PGF rs2268615, TNFAIP2 rs710100, PGF rs8019391

0.587

0.550

10/10

2.11 (1.56–2.84)< 0.0001

MDR multifactor dimensionality reduction, Bal. Acc. balanced accuracy, CVC cross–validation consistency, OR odds ratio, CI confidence interval
p values were calculated using χ2 tests
p < 0.05 indicates statistical significanceAiniwaer et al. BMC Medical Genetics(2020) 21:212

association of PGF and TNFAIP2 polymorphisms withCC risk, well-designed large and prospective studies are
required to validate our findings.

Page 7 of 76.

7.

Supplementary information8.

Supplementary information accompanies this paper at https://doi.org/10.
1186/s12881-020-01144-5.

9.

Additional file 1: Supplementary Table 1. Predicted stability, duplex
formation, and functional effect of 3′-UTR polymorphisms in miRNAbinding sites of selected genes. Supplementary Table 2. Primers sequence of PCR and UEP used in this study. Supplementary Table 3.
The information and HWE about the candidate SNPs.

10.

11.

Additional file 2.
12.
Abbreviations
CC: Cervical cancer; SNPs: Single nucleotide polymorphisms; PGF: Placental
growth factor; TNFAIP2: TNF alpha induced protein 2
Acknowledgements
The authors thank all the participants in this study.
Authors’ contributions
ZA, RM and LLH designed this study protocol and drafted the manuscript.
JL1, SH and JL2 performed the data analysis. RM, MN and LLH conceived
and supervised the study. All of these authors contributed to this article. The
author(s) read and approved the final manuscript.
Funding
This article was financially supported by the National Natural Science
Foundation of China (No. 81760467).
Availability of data and materials
The datasets generated and/or analysed during the current study areavailable in the Additional file 2.
Ethics approval and consent to participate
This study protocol was approved by the Ethics Committee of the People’s
Hospital of Xinjiang Uygur Autonomous Region and under the guidelines of
the Declaration of Helsinki. Written informed consent was obtained from allparticipants.

13.

14.

15.

16.

17.

18.

19.

20.

21.

Consent for publication
Not applicable.

22.

Competing interests
The authors declare that they have no competing interests.

23.

Received: 15 May 2020 Accepted: 12 October 2020

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